1. Field of the Invention
The present invention relates to the field of telecommunications and networking, and more particularly, a method and apparatus for controlling connection admission in a network with multiple service classes and quality of service requirements.
2. Description of Related Art
A critical need in managing the resources of a network with multiple service classes and quality of service (QoS) requirements is an effective connection admission control (CAC) algorithm which maximizes the utilization of the network resources while ensuring that QoS requirements of the admitted connections are met. As connection setup requests are received by the network and a transmission path is selected, a real-time local decision needs to be made to accept or reject the request based on the QoS and traffic parameters of the requesting connection as well as the state of the network including resource availability, traffic characteristics, and QoS requirements of the existing connections. Similarly, as connection release messages arrive, appropriate actions need to be taken to release any committed resources.
For example, in an asynchronous transfer mode (ATM) network there exist five service classes: Constant Bit Rate (CBR), real-time Variable Bit Rate (rtVBR), non-real-time Variable Bit Rate (nrtVBR), Available Bit Rate (ABR), and Unspecified Bit Rate (UBR). Of these five service classes, the rtVBR service is the most involved. Indeed, it is the only service category among all guaranteed service categories (CBR, rtVBR, and VBR) that combines both traffic burstiness and multiple QoS requirements (cell loss and delay) since CBR is not bursty and nrtVBR has only cell loss requirements. The other two service categories (ABR and UBR) are mainly best-effort in nature except for a minimum cell rate (MCR) component, which is met by simply reserving bandwidth equal to MCR.
Consider a set of virtual circuits (VCs) having the same QoS requirements given by L, D, and α representing the cell loss ratio (CLR), cell delay variation (CDV), and CDV percentile, respectively. If these VCs are allocated a buffer B and capacity C, then the CLR and CDV requirements are met whenProb(Qt>B)≦L, and  (1)Prob(Dt>D)≦α  (2)where Qt represents the queue length process and Dt represents the delay process. Since the delay and queue length are related through Dt=Qt/C, (2) can be rewritten asProb(Qt>D·C)≦α  (3)
The existing CAC algorithms for VBR services do not provide a complete solution and fall into one of the following categories where they either (a) ignore CDV and deal only with CLR and consequently they are applicable to nrtVBR only, or (b) assume a cell loss dominant model where CDV is not binding, or (c) assume a delay dominant model where CLR is not binding and convert the delay requirement into a loss (or buffer overflow) requirement as in equation (3), or (d) assume that the bandwidth and buffer allocations along the QoS requirements are such that the loss and delay performance are violated with the same rate, and therefore assume that D·C=B and α=L so that equation (1) and equation (3) are the same. Moreover, most existing models which deal with “first in, first out” (FIFO) scheduling explicitly model per-VC queuing using assumption (d) above.